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Increasingly complex 3D CAD models are essential during different life-cycle stages of modern engineering projects. Even though these models contain several repeated geometries, instancing information is often not available, resulting in increased requirements for storage, transmission, and rendering. Previous research have successfully applied shape matching techniques to identify repeated geometries and thus reduce memory requirements and improve rendering performance. However, these approaches require consistent vertex topology, prior knowledge about the scene, and/or the laborious creation of labeled datasets. In this paper, we present an unsupervised deep-learning method that overcomes these limitations and is capable of identifying repeated geometries and computing their instancing transformations. The method also guarantees a maximum visual error and preserves intrinsic characteristics of surfaces. Results on real-world 3D CAD models demonstrate the effectiveness of our approach: the datasets are reduced by up to 83.93% in size. Our approach achieves better results than previous work that does not rely on supervised learning. The proposed method is applicable to any kind of 3D scene and geometry. [Display omitted] • Unsupervised deep learning method for 3D shape registration. • Does not require any previous knowledge of the 3D geometries. • Does not require a labeled dataset for any supervised training. • Guarantees an upper bound on any visual errors. • Generalizes for any 3D scene and geometry. [ABSTRACT FROM AUTHOR]

Shape descriptors have recently gained popularity in shape matching, statistical shape modeling, etc. Their discriminative ability and efficiency play a decisive role in these tasks. In this paper, we first propose a novel handcrafted anisotropic spectral descriptor using Chebyshev polynomials, called the anisotropic Chebyshev descriptor (ACD); it can effectively capture shape features in multiple directions. The ACD inherits many good characteristics of spectral descriptors, such as being intrinsic, robust to changes in surface discretization, etc. Furthermore, due to the orthogonality of Chebyshev polynomials, the ACD is compact and can disambiguate intrinsic symmetry since several directions are considered. To improve the ACD's discrimination ability, we construct a Chebyshev spectral manifold convolutional neural network (CSMCNN) that optimizes the ACD and produces a learned ACD. Our experimental results show that the ACD outperforms existing state-of-the-art handcrafted descriptors. The combination of the ACD and the CSMCNN is better than other state-of-the-art learned descriptors in terms of discrimination, efficiency, and robustness to changes in shape resolution and discretization. [ABSTRACT FROM AUTHOR]

Abstract Shape descriptors have recently gained popularity in shape matching, statistical shape modeling, etc. Their discriminative ability and efficiency play a decisive role in these tasks. In this paper, we first propose a novel handcrafted anisotropic spectral descriptor using Chebyshev polynomials, called the anisotropic Chebyshev descriptor (ACD); it can effectively capture shape features in multiple directions. The ACD inherits many good characteristics of spectral descriptors, such as being intrinsic, robust to changes in surface discretization, etc. Furthermore, due to the orthogonality of Chebyshev polynomials, the ACD is compact and can disambiguate intrinsic symmetry since several directions are considered. To improve the ACD’s discrimination ability, we construct a Chebyshev spectral manifold convolutional neural network (CSMCNN) that optimizes the ACD and produces a learned ACD. Our experimental results show that the ACD outperforms existing state-of-the-art handcrafted descriptors. The combination of the ACD and the CSMCNN is better than other state-of-the-art learned descriptors in terms of discrimination, efficiency, and robustness to changes in shape resolution and discretization.

A new algorithm is presented to compute nonrigid, possibly partial comparisons of shapes defined by unstructured triangulations of their surfaces. The algorithm takes as input a pair of surfaces with each surface given by a distinct and unrelated triangulation. Its goal is to define a possibly partial correspondence between the vertices of the two triangulations, with a cost associated with this correspondence that can serve as a measure of the similarity of the two shapes. To find this correspondence, the vertices in each triangulation are characterized by a signature vector of features. We tested both the LD-SIFT signatures, based on the concept of spin images, and the wave kernel signatures obtained by solving the Shrödinger equation on the triangulation. A cost matrix C is constructed such that C (k , l) is the norm of the difference of the signature vectors of vertices k and l. The correspondence between the triangulations is then computed as the transport plan that solves the optimal transport or optimal partial transport problem between their sets of vertices. We use a statistical physics approach to solve these problems. The presentation of the proposed algorithm is complemented with examples that illustrate its effectiveness and manageable computing cost. [ABSTRACT FROM AUTHOR]

Shape matching is a fundamental problem in computer graphics with many applications. Functional maps translate the point-wise shape-matching problem into its functional counterpart and have inspired numerous solutions over the last decade. Nearly all the solutions based on functional maps rely on the eigenfunctions of the Laplace–Beltrami Operator (LB) to describe the functional spaces defined on the surfaces and then convert the functional correspondences into point-wise correspondences. However, this final step is often error-prone and inaccurate in tiny regions and protrusions, where the energy of LB does not uniformly cover the surface. We propose a new functional basis Principal Components of a Dictionary (PC D) to address such intrinsic limitation. PC D constructs an orthonormal basis from the Principal Component Analysis (PCA) of a dictionary of functions defined over the shape. These dictionaries can target specific properties of the final basis, such as achieving an even spreading of energy. Our experimental evaluation compares seven different dictionaries on established benchmarks, showing that PC D is suited to target different shape-matching scenarios, resulting in more accurate point-wise maps than the LB basis when used in the same pipeline. This evidence provides a promising alternative for improving correspondence estimation, confirming the power and flexibility of functional maps. [Display omitted] • A procedure to adaptively define basis for functional spaces over discrete 3D meshes • A general method that applies to different redundant dictionaries of functions • Achieve state-of-the-art shape matching accuracy in a functional map framework • Disclose the impact of more even distribution of the energy of a basis in matching • Quantitative assessment of seven bases in comparison to existing alternatives [ABSTRACT FROM AUTHOR]

It has been well known for decades that prone positioning (PP) improves oxygenation. However, it has gained widespread acceptance only in the last few years since studies have shown significant survival benefit. Many centers have established prone ventilation in their treatment algorithm for mechanically ventilated patients with severe acute respiratory distress syndrome (ARDS). Physiologically, PP should also benefit awake, non-intubated patients with acute hypoxemic respiratory failure. However, proning in non-intubated (PINI) patients did not gain any momentum until a few months ago when the Coronavirus disease 2019 (COVID-19) pandemic surged. A large number of sick patients overwhelmed the health care system, and many centers faced a dearth of ventilators. In addition, outcomes of patients placed on mechanical ventilation because of COVID-19 infection have been highly variable and often dismal. Hence, increased focus has shifted to using various strategies to prevent intubation, such as PINI. There is accumulating evidence that PINI is a low-risk intervention that can be performed even outside intensive care unit with minimal assistance and may prevent intubation in certain patients with ARDS. It can also be performed safely at smaller centers and, therefore, may reduce the patient transfer to larger institutions that are overwhelmed in the current crisis. We present a case series of 2 patients with acute hypoxemic respiratory failure who experienced significant improvements in oxygenation with PP. In addition, the physiology of PP is described, and concerns such as proning in obese and patient's anxiety are addressed; an educational pamphlet that may be useful for both patients and health care providers is provided.

As a linear actuator, accurate dimension measurement is crucial to the transmission reliability and interchangeability of ball screws. However, most of the current approaches are ineligible for rapid ball screw in-situ inspections due to the installation condition requirement of the production line. In this research, a machine vision method is presented to achieve highly accurate measurements of crucial parameters (the center distance and raceway arcs) in ball screws using a curvature edge detection algorithm. To capture images of the immediate area surrounding the area of interest, a telecentric lens is used. Thereafter, the curvature-based edge detection algorithm is employed to extract the contours. The measurement location on the object is automatically chosen by using a shape-matching algorithm. Additionally, random noise is suppressed by using the multiple-measurement averaging technique. Based on the results of the experiments, it is concluded that the center distance and the two raceway arcs computed absolute errors are 0.0019 mm, 0.0055 mm, and 0.0059 mm, respectively.

An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67  mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.

A new algorithm is presented to compute nonrigid, possibly partial comparisons of shapes defined by unstructured triangulations of their surfaces. The algorithm takes as input a pair of surfaces with each surface given by a distinct and unrelated triangulation. Its goal is to define a possibly partial correspondence between the vertices of the two triangulations, with a cost associated with this correspondence that can serve as a measure of the similarity of the two shapes. To find this correspondence, the vertices in each triangulation are characterized by a signature vector of features. We tested both the LD-SIFT signatures, based on the concept of spin images, and the wave kernel signatures obtained by solving the Shrödinger equation on the triangulation. A cost matrix C is constructed such that C(k,l) is the norm of the difference of the signature vectors of vertices k and l. The correspondence between the triangulations is then computed as the transport plan that solves the optimal transport or optimal partial transport problem between their sets of vertices. We use a statistical physics approach to solve these problems. The presentation of the proposed algorithm is complemented with examples that illustrate its effectiveness and manageable computing cost.

Two-dimensional (2D) irregular packing problems are widespread in manufacturing industries such as shipbuilding, metalworking, automotive production, aerospace, clothing and furniture manufacturing. Research on 2D irregular packing problems is essential for improving material utilization and industrial automation. Much research has been conducted on this problem with significant research results and certain algorithms. The work has made important contributions to solving practical problems. This paper reviews recent advances in the domain of 2D irregular packing problems based on a variety of research papers. We first introduce the basic concept and research background of 2D irregular packing problems and then summarize algorithms and strategies that have been proposed for the problems in recent years. Conclusion summarize development trends and research hotspots of typical 2D irregular shape packing problems. We hope that this review could provide guidance for researchers in the field of 2D irregular packing.

Salp swarm algorithm (SSA) is one of the efficient recent meta‐heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields. In the research process, it is found that it is very difficult to maintain the balance between the exploration and exploitation capabilities of a certain algorithm. Therefore, one of the main purposes of this article is to provide an algorithm that can intelligently balance between exploration and exploitation, so that it can balance exploration and exploitation capabilities. Later, in the research process, it was found that the sine and cosine function and the salp foraging trajectory have a high mathematical similarity, which greatly improves the optimization ability of the algorithm. In addition, the variable neighbourhood strategy can appropriately expand the optimization range of the algorithm. So in this paper, a novel golden sine cosine salp swarm algorithm with variable neighbourhood search scheme (GSCSSA‐VNS) is proposed, the another objective of proposing this algorithm is as a new optimization method for shape matching. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost effective in the computation process. Experimental results of four realistic examples show that GSCSSA‐VNS is able to provide very competitive results and outperforms the other algorithms. [ABSTRACT FROM AUTHOR]

Shape matching is a fundamental operation in digital geometry processing and computer graphics. Challenges in shape matching include finding correspondences of partial shapes with deformations, as well as topological noise and ambiguities. This paper presents a partial shape correspondence algorithm based on the concept of the functional map. An iterative dense matching algorithm, incorporating sparse and guided dense matching, is proposed along with a new objective function including both descriptor matching error and transformation error. Rank estimation with the rank direction is proposed to achieve more accurate slope approximation of the functional map. The slope is beneficial because it directly influences the matching efficiency. The experimental results obtained using FAUST and SHREC′16 datasets demonstrate the effectiveness of our proposed algorithm for matching the shapes of different human subjects and shapes with large missing parts compared with state-of-the art algorithms. The proposed algorithm provides an average geodesic distance of

Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision. While a number of correspondence selection methods have been proposed in recent years, their advantages and shortcomings remain unclear regarding different applications and perturbations. To fill this gap, this paper gives a comprehensive evaluation of nine state-of-the-art 3D correspondence grouping methods. A good correspondence grouping algorithm is expected to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall as well as facilitating accurate transformation estimation. Toward this rule, we deploy experiments on three benchmarks with different application contexts, including shape retrieval, 3D object recognition, and point cloud registration. We also investigate various perturbations such as noise, point density variation, clutter, occlusion, partial overlap, different scales of initial correspondences, and different combinations of keypoint detectors and descriptors. The rich variety of application scenarios and nuisances result in different spatial distributions and inlier ratios of initial feature correspondences, thus enabling a thorough evaluation. Based on the outcomes, we give a summary of the traits, merits, and demerits of evaluated approaches and indicate some potential future research directions. [ABSTRACT FROM AUTHOR]

Plant species recognition using leaf images is a highly important and challenging issue in botany and pattern recognition. A center problem of this task is how to accurately extract leaf image characteristics and quickly calculate the similarity between them. This article presents a new shape description approach called triangle-distance representation (TDR) for plant leaf recognition. The TDR descriptor is represented by two matrices: a sign matrix and a triangle center distance matrix. The sign matrix is used to characterize the convex/concave property of a shape contour, while the triangle center distance matrix is used to represent the bending degree and spatial information of a shape contour. This method can effectively capture the detailed and global characteristics of a leaf shape while keeping the similarity transformations (translation, rotation, and scaling) unchanged. In addition, this method is quite compact and has low computational complexity. We tested our method on four standard plant leaf datasets, including the famous Swedish, Smithsonian, Flavia, and ImageCLEF2012 datasets. The results confirm that our approach exceeds the prior state-of-the-art shape-based plant leaf recognition approaches. An extra experiment on the MPEG-7 shape dataset further shows that our method can be applied to general shape recognition.

Although the total number of ureteroscopy interventions during the past years has significantly increased, current flexible ureteroscopy procedures still present some limitations to urologic surgeons. However, nowadays different robotic systems have been developed in order to reduce those limitations. Flexible ureteroscopy robots provide a technological alternative which combines the benefits that this type of procedures offers to the patients, and solutions to the problems encountered from the surgeons perspective. In this paper, a virtual reality training platform for robot-assisted flexible ureterorenoscopy interventions is presented. A position based model for the virtual flexible endoscope is detailed and a standard user interface for the training platform is designed. Moreover, a comparative analysis of the performance of the training platform in different scenarios, including the navigation through a three-dimensional ureterorenal model, is presented. The obtained results determine that the training platform presents different computational rates depending on the complexity of the implemented environment and on the number of collisions and constraints that have to be handled. Nevertheless, the virtual model is visually plausible, effective for real-time user interaction and suitable for training.

Shape matching has been extensively used in various fields. The local feature-based or global feature-based algorithms can hardly describe the shape comprehensively due to their inherent defects. Combining the local and global feature to describe the shape has become a trend. In this paper, an improved discrete curve evolution algorithm is proposed which combines the discrete curve evolution with the uniform sampling and achieves a better description of the shape contour. Three simple and intuitive multi-scale features which represent both the global and local features of shapes are designed from aspects of the spatial relationship of contour points, structural information of contour sequence, and shape geometry feature. A cyclic Smith-Waterman algorithm is introduced to solve local contour matching and starting point selection. Experimental results demonstrate that our proposed features are translation, rotation, and scaling invariant, and have good robustness to deformation. Retrieval accuracies of Kimia99, Kimai216, and MPEG-7 indicate that our method can bring out a better performance.

Linking arbitrary shapes (e.g., circles, squares, and triangles) to personal labels (e.g., self, friend, or stranger) or reward values (e.g., £18, £6, or £2) results in immediate processing benefits for those stimuli that happen to be associated with the self or high rewards in perceptual matching tasks. Here we further explored how social and reward associations interact with multisensory stimuli by pairing labels and objects with tones (low, medium, and high tones). We also investigated whether self and reward biases persist for multisensory stimuli with the label removed after an association had been made. Both high reward stimuli and those associated with the self, resulted in faster responses and improved discriminability (i.e., higher d’), which persisted for multisensory stimuli even when the labels were removed. However, these self- and reward-biases partly depended on the specific alignment between the physical tones (low, medium, and high) and the conceptual (social or reward) order. Performance for reward associations improved when the endpoints of low or high rewards were paired with low or high tones; meanwhile, for personal associations, there was a benefit when the self was paired with either low or high tones, but there was no effect when the stranger was associated with either endpoint. These results indicate that, unlike reward, social personal associations are not represented along a continuum with two marked endpoints (i.e., self and stranger) but rather with a single reference point (the self vs. other).

Linking arbitrary shapes (e.g., circles, squares, and triangles) to personal labels (e.g., self, friend, or stranger) or reward values (e.g., £18, £6, or £2) results in immediate processing benefits for those stimuli that happen to be associated with the self or high rewards in perceptual matching tasks. Here we further explored how social and reward associations interact with multisensory stimuli by pairing labels and objects with tones (low, medium, and high tones). We also investigated whether self and reward biases persist for multisensory stimuli with the label removed after an association had been made. Both high reward stimuli and those associated with the self, resulted in faster responses and improved discriminability (i.e., higher d'), which persisted for multisensory stimuli even when the labels were removed. However, these self- and reward-biases partly depended on the specific alignment between the physical tones (low, medium, and high) and the conceptual (social or reward) order. Performance for reward associations improved when the endpoints of low or high rewards were paired with low or high tones; meanwhile, for personal associations, there was a benefit when the self was paired with either low or high tones, but there was no effect when the stranger was associated with either endpoint. These results indicate that, unlike reward, social personal associations are not represented along a continuum with two marked endpoints (i.e., self and stranger) but rather with a single reference point (the self vs. other). [ABSTRACT FROM AUTHOR]

Among the structures that make up a smart city, tunnels are recognized as an important facility in the US and Canada as an infrastructure for the construction of an eco-friendly city. Total station surveying takes a long time to acquire data and has the disadvantage that the Global Navigation Satellite System (GNSS) needs to be able to receive satellites in tunnel surveying. Recently, 3D laser scanners have been attracting attention as a new technology to compensate for the disadvantages of a small amount of data and a long measurement time of the total station. Three-dimensional laser scanners can be used in various fields. In civil engineering, research and application methods often use 3D laser scanners. Because tunnel surveying is difficult in a GNSS survey due to site conditions, total station surveying has been mainly used. As total station surveying takes a lot of time to acquire data, the use of 3D laser scanners for tunnel surveying has recently been increasing. However, when using a 3D laser scanner, a large number of reference points must be installed inside the tunnel for registration and geo-referencing of acquired data. As a new technology, a shape matching method has been introduced in the field of scanning sensors, and its use is expected. In this study, a tunnel survey using a 3D laser scanner was performed and its utility was evaluated. Also, the accuracy of the shape registration method for recent registration was analyzed. The scanning data were acquired by configuring a traverse using the total station function and compared with 10 checkpoints already installed for accuracy verification. As a result of the accuracy evaluation by comparison with the checkpoints, the maximum error was within 6 cm in the N, E, and H directions, and these results indicate the plane and elevation allowable accuracy of a 1:1000 digital map, suggesting the applicability of the method using the reference point performance and the laser scanning data. By using scan data, not only analysis of the cross section but also continuous analysis of the shape of the scanning section is possible. An additional evaluation of the accuracy of shape matching of the 3D laser scanner data was performed. By using the scanning data acquired five times, geo-referencing of the data acquired at the first station was performed, and then the data obtained were registered by the shape matching method. A maximum of error of 1 m occurred at the end of the data. This indicates that the accuracy of the shape registration can be degraded if the same shape is repeated, such as in a tunnel. Therefore, in tunnel surveying, it is judged that the construction of the data only by shape matching may lower the accuracy. Further research can improve the accuracy of the shape registration method, which will improve the efficiency of tunnel surveying. [ABSTRACT FROM AUTHOR]

This article presents a new numerical method for facial reconstruction. The problem is the following: given a dry skull, reconstruct a virtual face that would help in the identification of the subject. The approach combines classical features as the use of a skulls/faces database and more original aspects: (1) an original shape matching method is used to link the unknown skull to the database templates; (2) the final face is seen as an elastic 3D mask that is deformed and adapted onto the unknown skull. In this method, the skull is considered as a whole surface and not restricted to some anatomical landmarks, allowing a dense description of the skull/face relationship. Also, the approach is fully automated. Various results are presented to show its efficiency.

Placental chorionic surface vascular networks (PCSVNs) are essential high-capacitance, low-resistance distribution and drainage networks, and are hence important to placental function and to fetal and newborn health. It was hypothesized that variations in the PCSVN structure may reflect both the overall effects of genetic and environmentally regulated variations in branching morphogenesis within the conceptus and the fetus’s vital organs. A critical step in PCSVN analysis is the extraction of blood vessel structure, which has only been done manually through a laborious process, making studies in large cohorts and applications in clinical settings nearly impossible. The large variation in the shape, color, and texture of the placenta presents significant challenges to both machine and human to accurately extract PCSVNs. To increase the visibility of the vessels, colored paint can be injected into the vascular networks of placentas, allowing PCSVNs to be manually traced with a high level of accuracy. This paper provides a proof-of-concept study to explain the geometric differences between manual tracings of paint-injected and un-manipulated PCSVNs under the framework of a shape-context model. Under this framework, paint-injected and un-manipulated tracings of PCSVNs can be matched with nearly 100% accuracy. The implication of our results is that the manual tracing protocol yields faithful PCSVN representations modulo a set of affine transformations, making manual tracing a reliable method for studying PCSVNs. Our work provides assurance to a new pre-processing approach for studying vascular networks by ways of dye-injection in medical imaging problems.

Abstract Shape matching plays an important role in various computer vision and graphics applications such as shape retrieval, object detection, image editing, image retrieval, etc. However, detecting shapes in cluttered images is still quite challenging due to the incomplete edges and changing perspective. In this paper, we propose a novel approach that can efficiently identify a queried shape in a cluttered image. The core idea is to acquire the transformation from the queried shape to the cluttered image by summarising all point-to-point transformations between the queried shape and the image. To do so, we adopt a point-based shape descriptor, the pyramid of arc-length descriptor (PAD), to identify point pairs between the queried shape and the image having similar local shapes. We further calculate the transformations between the identified point pairs based on PAD. Finally, we summarise all transformations in a 4D transformation histogram and search for the main cluster. Our method can handle both closed shapes and open curves, and is resistant to partial occlusions. Experiments show that our method can robustly detect shapes in images in the presence of partial occlusions, fragile edges, and cluttered backgrounds.

In this paper, a novel systematic method on the evaluation of quantitative precipitation forecast (QPF) errors from the perspective of rain-area shape verification is proposed. The method aims to improve the accuracy and efficiency of conventional station-based verifications (i.e., standard skill scores), which are insensitive to the biases of station location and rain-area shape and tend to ignore the continuity of precipitation in time and space. The method develops and combines the shape verification indexes, which include the overlap ratio of a forecasted rain-area (Ratiof), the overlap ratio of the ground rainarea (Ratiot), the Jaccard similarity coefficient between the shape of the QPF area, the shape of the ground rain-area (Jaccardshape), the critical success index (CSI) for the rain-area shape (CSIshape), the probability of detection (POD) for the rain-area shape (PODshape), and the false alarm ratio (FAR) for the rain-area shape (FARshape). This definition of QPF verification is applied to a rain event from 2016/08/02 00:30 to 2016/08/02 03:24 in the Guangdong Province. The decomposition of QPF errors into station-based errors and shape error components provides powerful insight into the effects of overall forecasting performance. The experimental results of this investigation show that the proposed method provides an opportunity to assess QPFs objectively and further promote advanced forecasting technologies from this perspective.

A sketch map represents an individual's perception of a specific location. However, the information in sketch maps is often distorted and incomplete. Nevertheless, the main roads of a given location often exhibit considerable similarities between the sketch maps and metric maps. In this work, a shape-based approach was outlined to align roads in the sketch maps and metric maps. Specifically, the shapes of main roads were compared and analyzed quantitatively and qualitatively in three levels pertaining to an individual road, composite road, and road scene. An experiment was performed in which for eight out of nine maps sketched by our participants, accurate road maps could be obtained automatically taking as input the sketch and the metric map. The experimental results indicate that accurate matches can be obtained when the proposed road alignment approach Shape-based Spatial-Query-by-Sketch (SSQbS) is applied to incomplete or distorted roads present in sketch maps and even to roads with an inconsistent spatial relationship with the roads in the metric maps. Moreover, highly similar matches can be obtained for sketches involving fewer roads. [ABSTRACT FROM AUTHOR]

The term 'collective' is used to refer to a group of animals such as a flock of birds or a herd of elephants. Aggregate motions of such collectives often give rise to visually pleasing shapes and patterns (e.g. V-shape formation of geese while they migrate from one place to another). While shapes of moving collectives are of great interest in many scientific studies, scant attention has been given to algorithmically extract and render these shapes via polygonal boundaries or graphs. In this Letter, the authors present a multi-stage, proof of concept framework for tracking geometric shapes and extracting video frames containing a user defined shape of moving collectives, by employing a deep-learning based object detection, well-known alpha shapes and a modified shape context. They demonstrate the usefulness of the proposed framework on a couple of test videos and discuss its potential applications in a wider area. [ABSTRACT FROM AUTHOR]

In this work, we present a novel, theoretical approach to address one of the longstanding problems in computer vision: 2D and 3D affine invariant feature matching. Our proposed Grassmannian Graph (GrassGraph) framework employs a two stage procedure that is capable of robustly recovering correspondences between two unorganized, affinely related feature (point) sets. In the ideal case, the first stage maps the feature sets to an affine invariant Grassmannian representation, where the features are mapped into the same subspace. It turns out that coordinate representations extracted from the Grassmannian differ by an arbitrary orthonormal matrix. In the second stage, by approximating the Laplace-Beltrami operator (LBO) on these coordinates, this extra orthonormal factor is nullified, providing true affine invariant coordinates which we then utilize to recover correspondences via simple mutual nearest neighbor relations. Our validation benchmarks use large number of experimental trials performed on 2D and 3D datasets. Experimental results show that the proposed Grass-Graph method successfully recovers large affine transformations. [ABSTRACT FROM AUTHOR]

Background: Direct comparison of 2D images is computationally inefficient due to the need for translation, rotation, and scaling of the images to evaluate their similarity. In many biological applications, such as digital pathology and cryo-EM, often identifying specific local regions of images is of particular interest. Therefore, finding invariant descriptors that can efficiently retrieve local image patches or subimages becomes necessary. Results: We present a software package called Two-Dimensional Krawtchouk Descriptors that allows to perform local subimage search in 2D images. The new toolkit uses only a small number of invariant descriptors per image for efficient local image retrieval. This enables querying an image and comparing similar patterns locally across a potentially large database. We show that these descriptors appear to be useful for searching local patterns or small particles in images and demonstrate some test cases that can be helpful for both assembly software developers and their users. Conclusions: Local image comparison and subimage search can prove cumbersome in both computational complexity and runtime, due to factors such as the rotation, scaling, and translation of the object in question. By using the 2DKD toolkit, relatively few descriptors are developed to describe a given image, and this can be achieved with minimal memory usage. [ABSTRACT FROM AUTHOR]

Effectively describing and recognizing leaf shapes under arbitrary variations, particularly from a large database, remains an unsolved problem. In this research, we attempted a new strategy of describing leaf shapes by walking and measuring along a bunch of chords that pass through the shape. A novel chord bunch walks (CBW) descriptor is developed through the chord walking behavior that effectively integrates the shape image function over the walked chord to reflect both the contour features and the inner properties of the shape. For each contour point, the chord bunch groups multiple pairs of chords to build a hierarchical framework for a coarse-to-fine description that can effectively characterize not only the subtle differences among leaf margin patterns but also the interior part of the shape contour formed inside a self-overlapped or compound leaf. Instead of using optimal correspondence based matching, a Log-Min distance that encourages one-to-one correspondences is proposed for efficient and effective CBW matching. The proposed CBW shape analysis method is invariant to rotation, scaling, translation, and mirror transforms. Five experiments, including image retrieval of compound leaves, image retrieval of naturally self-overlapped leaves, and retrieval of mixed leaves on three large scale datasets, are conducted. The proposed method achieved large accuracy increases with low computational costs over the state-of-the-art benchmarks, which indicates the research potential along this direction. [ABSTRACT FROM AUTHOR]

Spatial predictions of wildfire spread are used operationally and in risk estimation. It is important that their outputs are validated to quantify predictive performance and uncertainty. There are numerous loss functions for this simulation validation. Our paper synthesises ten common and five novel loss functions for evaluating the performance of wildfire predictions against observed or simulated wildfires. We describe each loss function; test their sensitivity to scale, rotation and translation; and demonstrate their application with three case study wildfires in south-eastern Australia. Based on their purpose, there are three categories of loss functions: general overlap, partial overlap and distance. Within these categories, there is functional redundancy as many loss functions are appropriate and perform similarly. Using loss functions from across the three different categories is the most comprehensive for evaluating wildfire models, but the choice depends on the simulation purpose and interpretation. • Spatial predictions of wildfire spread are frequently used but not always evaluated. • We synthesised fifteen loss functions for evaluating predicted vs observed wildfire spread. • There is not a 'best' loss function, it depends on simulation use and interpretation. [ABSTRACT FROM AUTHOR]

The re-entrant structures are among the simple unit cell designs that have been widely used in the design of mechanical metamaterials. Changing the geometrical parameters of these unit cell structures, their overall elastic properties (i.e., elastic stiffness and Poisson’s ratio), can be simultaneously tuned. Therefore, different design strategies (e.g., functional gradient) can be implemented to design advanced engineering materials with unusual properties. Here, using the theory of elasticity and finite element modeling, we propose a fast and direct approach to effectively design the microarchitectures of mechanical metamaterials with re-entrant structures that allow predicting complex deformation shapes under uniaxial tensile loading. We also analyze the efficiency of this method by back calculating the microarchitectural designs of mechanical metamaterials to predict the complex 1-D external contour of objects (e.g., vase and foot). The proposed approach has several applications in creating programmable mechanical metamaterials with shape matching properties for exoskeletal and soft robotic devices.

Abstract Background In neuroscience research, mouse models are valuable tools to understand the genetic mechanisms that advance evidence-based discovery. In this context, large-scale studies emphasize the need for automated high-throughput systems providing a reproducible behavioral assessment of mutant mice with only a minimum level of manual intervention. Basic element of such systems is a robust tracking algorithm. However, common tracking algorithms are either limited by too specific model assumptions or have to be trained in an elaborate preprocessing step, which drastically limits their applicability for behavioral analysis. Results We present an unsupervised learning procedure that is basically built as a two-stage process to track mice in an enclosed area using shape matching and deformable segmentation models. The system is validated by comparing the tracking results with previously manually labeled landmarks in three setups with different environment, contrast and lighting conditions. Furthermore, we demonstrate that the system is able to automatically detect non-social and social behavior of interacting mice. The system demonstrates a high level of tracking accuracy and clearly outperforms the MiceProfiler, a recently proposed tracking software, which serves as benchmark for our experiments. Conclusions The proposed method shows promising potential to automate behavioral screening of mice and other animals. Therefore, it could substantially increase the experimental throughput in behavioral assessment automation.

Görsel kılavuzlanan bir mobil robotun baş etmesi gereken sorunlarından biri gece görüştür. Mobil robotların diğer bir uygulama alanı ise lider takibidir. Bu çalışmada bu amaçlara yönelik tasarlanmış Bilecik Mobil Robot (BLMρ) tanıtılmaktadır. Çalışmada gece görüş IR-LED kamera ile sağlanırken şekil taşıyan lideri bulma ve takip etme bölgesel tanımlayıcılar, 2B çapraz korelasyon ve şekil betimleyici ile başarılmıştır. Kalman filtresi ile hesapsal yük azaltılarak daha etkin bir takip algoritması oluşturulmuştur. BLMρ lider üzerindeki şeklin görsel geribesleme ile elde edilen merkezinin görüntüdeki konumunu ve BLMρ’ya uzaklığını belirten görüntüdeki alanını giriş olarak almaktadır. Bu girişleri kullanan bulanık mantık birimi sağ ve sol tekerler için kontrol işaretleri üretmektedir. Çalışmada belirtilen aşamalar ayrıntılarıyla verilmiş, lider takibi için kullanılan yöntemler karşılaştırılmış ve deneysel sonuçlar sunulmuştur.

Recognizing 3-D objects in cluttered scenes is a challenging task. Common approaches find potential feature correspondences between a scene and candidate models by matching sampled local shape descriptors and select a few correspondences with the highest descriptor similarity to identify models that appear in the scene. However, real scans contain various nuisances, such as noise, occlusion, and featureless object regions. This makes selected correspondences have a certain portion of false positives, requiring adopting the time-consuming model verification many times to ensure accurate recognition. This paper proposes a 3-D object recognition approach with three key components. First, we construct a Signature of Geometric Centroids descriptor that is descriptive and robust, and apply it to find high-quality potential feature correspondences. Second, we measure geometric compatibility between a pair of potential correspondences based on isometry and three angle-preserving components. Third, we perform effective correspondence selection by using both descriptor similarity and compatibility with an auxiliary set of “less” potential correspondences. Experiments on publicly available data sets demonstrate the robustness and/or efficiency of the descriptor, selection approach, and recognition framework. Comparisons with the state-of-the-arts validate the superiority of our recognition approach, especially under challenging scenarios.

Shape classification and matching is an important branch of computer vision. It is widely used in image retrieval and target tracking. Shape context method, curvature scale space (CSS) operator and its improvement have been the main algorithms of shape matching and classification. The shape classification network (SCN) algorithm is proposed inspired by LeNet5 basic network structure. Then, the network structure of SCN is introduced and analyzed in detail, and the specific parameters of the network structure are explained. In the experimental part, SCN is used to perform classification tasks on three shape datasets, and the advantages and limitations of our algorithm are analyzed in detail according to the experimental results. SCN performs better than many traditional shape classification algorithms. Accordingly, a practical example is given to show that SCN can save computing resources.

• Efficient algorithm for computing the two-sided Hausdorff distance between a triangle mesh and a quad mesh. • Specialized algorithm for handling the cases of near-zero Hausdorff distance in an interactive speed. • Guarantee on the approximation error of the Hausdorff distance to be within an arbitrarily given error bound, which can be machine precision-level small. Two-sided Hausdorff Distance Computation Progress We introduce an algorithm for computing the two-sided Hausdorff distance between a triangle mesh and a quad mesh, guaranteed to be within the given error bound, which can be machine precision-level small. The algorithm expands upon a recent breakthrough that only calculates the one-sided Hausdorff distance from the triangle mesh to the quad mesh using what is called "matching" and "upper bounding" of candidate pieces. We complete the algorithm by accomplishing the computation of the one-sided Hausdorff distance in the opposite direction: from the quad mesh to the triangle mesh. We split each quad into two triangular pieces to simplify the breakdown of matching cases and provide additional matching methods for new cases. By fusing the two one-sided computation algorithms, one can compute the two-sided Hausdorff distance that, for instance, can properly evaluate a quad mesh approximation of a triangle mesh. Experimental results show that our algorithm can handle near-zero Hausdorff distance, which has always been known to be a much difficult task, in an interactive time. Moreover, the improvement in efficiency of the two-sided Hausdorff distance computation over the successive execution of the two one-sided computations is addressed. [ABSTRACT FROM AUTHOR]

In this study, a novel method of pattern recognition for static gesture recognition is proposed. To deal with this application, the authors should tackle the problem of low variability among shape classes in the gestures databases. The authors' method is based on robust registration and shape geodesics in shape space with a preliminary step of pose estimation accelerating the processing time. The different gestures are considered individual points in a non‐linear shape space. Similarity between any two considered shapes can be measured by a distance metric on the shape space. For shape comparison, three distances with and without robust norm are proposed and evaluated for the target application. To cope with low shape variability among classes, the best scheme is to use robust norm in shape matching but not in shape comparison. Experiments are conducted on reference databases used in the literature to evaluate static gesture recognition. These experiments show the outperformance of the proposed scheme compared to state‐of‐the art methods. [ABSTRACT FROM AUTHOR]

• We apply 3D extension of the features proposed by Jaeger et al. and a comparative analysis between 2D and 3D features in the context of freehand gesture recognition. It have also shown that, these 3D features perform better than existing 3D features. • A gesture recognition system through mapping of shapes with hand gestures. • A dataset comprises with a total of 5400 hand gesture samples to the research community. Designing of touchless user interface is gaining popularity in various contexts. Users can interact with electronic devices using such interfaces even when their hands are dirty or non-conductive. Also, users with partial physical disability can interact with electronic devices with the help of touchless interfaces. In this paper, we propose a Leap Motion controller-based methodology to facilitate rendering of 2D and 3D shapes on display devices. The proposed method tracks finger movements while users perform natural gestures within the field of view of the motion sensor. Then, trajectories are analyzed to extract extended Npen++ features in 3D. These features capture finger movements during the gestures and they are fed to unidirectional left-to-right Hidden Markov Model (HMM) for training. A one-to-one mapping between gestures and shapes, is proposed. Finally, the shapes corresponding to these gestures are rendered over the display using a typical MuPad supported interface. We have created a dataset of 5400 samples recorded by 10 volunteers. Our dataset contains 18 geometric and 18 non-geometric shapes such as "circle", "rectangle", "flower", "cone", "sphere", etc. The proposed method has achieved 92.87% accuracy using a 5-fold cross validation scheme. Experiments reveal that the extended 3D features perform better than the existing 3D features when applied for shape representation and classification. The method can be used for developing diverse HCI applications suitable for smart display devices. [ABSTRACT FROM AUTHOR]

Shape is considered to be one of the most promising tools to represent and recognise an object. In this study, an effective and rigorous shape matching algorithm is developed based on a new descriptor and relaxation labelling technique. For each contour point, the descriptor captures the distribution of all points within the shape region along the vector perpendicular to that from the centroid to the point. In addition to stable affine invariance, the descriptor is robust to noise since it makes use of all points in the shape region. The descriptor distance is used to initialise the contour point matching probability, and relaxation labelling technique is utilised to update the matching probability using a new compatibility coefficient function, which is defined based on the shape projection preserving characteristic. The experiments on synthetic and real remote sensing data are provided to test the performance of the authors’ proposed algorithm. Compared to other four state‐of‐the‐art contour‐based shape matching algorithms, their algorithm is more robust and capable of shape matching under affine transformations and noise.

A sketch map represents an individual’s perception of a specific location. However, the information in sketch maps is often distorted and incomplete. Nevertheless, the main roads of a given location often exhibit considerable similarities between the sketch maps and metric maps. In this work, a shape-based approach was outlined to align roads in the sketch maps and metric maps. Specifically, the shapes of main roads were compared and analyzed quantitatively and qualitatively in three levels pertaining to an individual road, composite road, and road scene. An experiment was performed in which for eight out of nine maps sketched by our participants, accurate road maps could be obtained automatically taking as input the sketch and the metric map. The experimental results indicate that accurate matches can be obtained when the proposed road alignment approach Shape-based Spatial-Query-by-Sketch (SSQbS) is applied to incomplete or distorted roads present in sketch maps and even to roads with an inconsistent spatial relationship with the roads in the metric maps. Moreover, highly similar matches can be obtained for sketches involving fewer roads.

In this paper a shape matching algorithm for multiple component objects has been proposed which aims at matching shapes by a two-stage method. The first stage extracts the similarity features of each component using a generic shape representation model. The first stage of our shape matching method normalizes the components for orientation and scaling, and neglects minor deformations. In the second stage, the extracted similarity features of the components are combined with their relative spatial characteristics for shape matching. Some important application areas for the proposed multi-component shape matching are medical image registration, content based medical image retrieval systems, and matching articulated objects which rely on the a-priori information of the model being searched. In these applications, salient features such as vertebrae or rib cage bones can be easily segmented and used. These features however, show differences from person to person on one hand and similarities at different cross-sectional images of the same examination on the other hand. The proposed method has been tested on articulated objects, and reliable registration of 3-dimensional abdominal computed tomography images.

This article presents a new numerical method for facial reconstruction. The problem is the following: given a dry skull, reconstruct a virtual face that would help in the identification of the subject. The approach combines classical features as the use of a skulls/faces database and more original aspects: (1) an original shape matching method is used to link the unknown skull to the database templates; (2) the final face is seen as an elastic 3D mask that is deformed and adapted onto the unknown skull. In this method, the skull is considered as a whole surface and not restricted to some anatomical landmarks, allowing a dense description of the skull/face relationship. Also, the approach is fully automated. Various results are presented to show its efficiency. [ABSTRACT FROM AUTHOR]